Evaluating the Impact of Data Standardization on Real-World Data.

Abstract

Purpose: To understand the impact of standardizing administrative healthcare data to the Sentinel common data model for cohort selection and descriptive findings.

Methods: Among patients with an outpatient COVID-19 diagnosis (January 2021-December 2022) in HealthVerity using the data in its native and the standardized format, we descriptively compared cohort attrition and sample size, patient characteristics, and healthcare resource utilization during baseline and incidence of selected conditions after COVID-19 diagnosis.

Results: The standardized cohort included fewer patients than the native (164 445 vs. 198 317), but age (median 48 years) and sex (70% female) were the same. The distribution of race was similar; however, the standardized cohort mapped patients with "Other" race to the "Unknown/Missing" race category, which created differences among those categories. Distributions were similar, albeit slightly lower for comorbidities (differences < 1%), and lower for SARS-CoV-2 diagnostic tests (59% vs. 70%). Medical encounter counts were also lower, with substantial differences that were attenuated after limiting encounter counts to one event per day (e.g., mean count of 6.0 vs. 27.7 specialty care visits reduced to 2.9 vs. 3.5). Incidence rates were lower, with the greatest difference for hepatotoxicity (29.6 vs. 37.1 per 1000 person-years).

Conclusions: The data standardization refines the data (e.g., removes duplicate claims and variables or variable categories), which may reduce outliers and errors but yield lower distributions and counts of certain variables than observed in native format data. Therefore, it is critical to understand how standardization impacts the data and subsequently its fitness for use.